Seeram Chapter 11: Image Quality

1. CT Seeram Chapter 11: Image Quality

2. CT Image Quality Parameters

3. Factors InfluencingCT Image Quality

4. Spatial Resolution Quantifies image blurring “Ability to discriminate objects of varying density a small distance apart against a uniform background” Minimum separation required between two high contrast objects for them to be resolved as two objects

5. Spatial Resolution

6. Resolvable Object Size &Limiting Resolution Smallest resolvable high contrast object Often expressed as line pairs / cm “Pair” is one object + one space

7. Resolvable Object Size:Limiting Resolution Smallest resolvable high contrast object is half the reciprocal of spatial frequency Example: Limited resolution = 15 line pairs per cm Pair is 1/15th cm Object is half of pair 1/15th / 2 1/30th cm .033 cm 0.33 mm

8. Geometric Factors affectingSpatial Resolution Focal spot size detector aperture width slice thickness or collimation Less variation likely for thinner slices attenuation variations within a voxel are averaged partial volume effect

9. Geometric Factors affectingSpatial Resolution focal spot - isocenter distance

10. Geometric Unsharpness & CT Object details distributed (blurred) over several detectors decrease spatial resolution detector aperture size must be smaller than object for object to be resolved

11. Non-geometric Factorsaffecting Spatial Resolution # of projections Display matrix size 512 X 512 pixels standard Reconstruction algorithms smoothing or enhancing of edges

12. Reconstruction Algorithm &Spatial Resolution Back projecting process blurs image Algorithms may be anatomically specific Special algorithms edge enhancement noise reduction smoothing soft tissue or bone emphasis

13. Hi-Resolution CT Technique Very small slice thicknesses 1-2 mm High spatial frequency algorithms increases resolution increases noise Noise can be offset by using higher doses Optimized window / level settings Small field of view (FOV) Known as “targeting”

14. Contrast Resolution Ability of an imaging system to demonstrate small changes in tissue contrast The difference in contrast necessary to resolve 2 large areas in image as separate structures

15. CT Contrast Resolution Significantly better than radiography CT can demonstrate very small differences in density and atomic #

16. CT Contrast Resolution Depends Upon reconstruction algorithm low spatial frequency algorithm smooths image Loss of spatial resolution Reduces noise enhances perceptibility of low contrast lesions image display

17. CT Contrast Resolution Depends on Noise

18. CT Contrast Resolution Contrast depends on noise

19. # of Photons Detected Depends Upon photon flux (x-ray technique) slice thickness patient size Detector efficiency Note: Good contrast resolution requires that detector sensitivity be capable of discriminating small differences in intensity

22. CT Image Noise Fluctuation of CT #’s in an image of uniform material (water) Usually described as standard deviation of pixel values

23. CT Image Noise Standard deviation of pixel values

24. Noise Level Units CT numbers (HU’s)or % contrast Example CT # range: 1000 HU’s Standard deviation: 3 HU’s Noise level is 3 or 3 / 1000 X 100 = 0.3%

25. Noise Measurement in CT Scan water phantom Select regions of interest Take mean & standard deviation in each region Standard deviation is noise in ROI

26. CT Noise Levels Depend Upon # detected photons quantum noise

27. Photon Flux to Detectors Tube output flux (intensity) depends upon kVp mAs beam filtration Flux is combination of beam quality & quantity Flux to detectors modified by patient

28. Slice Thickness Thinner slices mean less scatter better contrast less active detector area less photons detected More noise To achieve equivalent noise with thinner slices, dose (technique factors) must be increased

29. Noise Levels in CT: Increasing slice width degrades spatial resolution less uniformity inside a larger pixel partial volume effect

30. CT Image Quality inEquation Form

31. Noise Levels in CT: When dose increases, noise decreases dose increases # detected photons Doubling spatial resolution (2X lp/mm) requires an 8X increase in dose for equivalent noise Smaller voxels mean less radiation per voxel

32. Story of CT Image Quality Equation

33. Measurements of Image Quality PSF = Point Spread Function LSF = Line Spread Function CTF = Contrast Transfer Function MTF = Modulation Traffic Function

34. Point Spread FunctionPSF “Point” object imaged as circle due to blurring Causes finite focal spot size finite detector size finite matrix size Finite separation between object and detector Ideally zero Finite distance to focal spot Ideally infinite

35. Quantifying Blurring Object point becomes image circle Difficult to quantify total image circle size difficult to identify beginning & end of object

36. Quantifying BlurringFull Width at Half Maximum (FWHM) width of point spread function at half its maximum value Maximum value easy to identify Half maximum value easy to identify Easy to quantify width at half maximum

37. Line Spread FunctionLSF Line object image blurred Image width larger than object width

38. Contrast Response FunctionCTF or CRF Measures contrast response of imaging system as function of spatial frequency

39. Contrast Response FunctionCTF or CRF Blurring causes loss of contrast darks get lighter lights get darker

40. CT Phantoms Available from CT manufacturer private phantom manufacturers American Association of Physicists in Medicine AAPM

41. CT Spatial vs. Contrast Resolution Spatial & contrast resolution interact High contrast objects are easier to resolve One can improve one at the expense of the other Can only improve both by increasing dose

42. Contrast & Detail Larger objects easy to see even at low contrast

43. Contrast & Detail Small objects only visible at high contrast

44. Contrast Detail Diagrams Contrast vs. object diameter less contrast means object must be larger to resolve Info on both low & high contrast CT resolution

45. Modulation Transfer FunctionMTF Fraction of contrast reproduced as a function of frequency

46. MTF Can be derived from point spread function line spread function MTF = 1 means all contrast reproduced at this frequency MTF = 0 means no contrast reproduced at this frequency

47. MTF If MTF = 1 all contrast reproduced at this frequency

48. MTF If MTF = 0.5 half of contrast reproduced at this frequency

49. MTF If MTF = 0 no contrast reproduced at this frequency

50. Component MTF Each component in an imaging system has its own MTF each component retains a fraction of contrast as function of frequency System MTF is product of MTF’s for each component. Since MTF is between 0 and 1, composite MTF <= MTF of poorest component

51. CT Number Calculated from reconstructed pixel attenuation coefficient

52. Linearity Linear relationship of CT #’s to linear attenuation coefficients of objects Checked with phantom of several known materials average CT # of each material obtained from ROI analysis Compare CT #’s with known coefficients

53. CatPhan

55. Cross-Field Uniformity Use uniform phantom (water) CT pixel values should be uniform anyplace in image Take 5 ROI 1 center ROI 4 corners ROI’s Compare standard deviation between ROI’s

56. CT ArtifactsDistortion Areas where image not faithful to subject Sources patient image process equipment

57. CT ArtifactsDistortion Phantoms with evenly distributed objects

58. Preview!CT Artifacts: Causes motion metal & high-contrast sharp edges beam hardening partial volume averaging sampling detectors

59. Motion Artifacts Causes streaks in image Algorithms have trouble coping because of inconsistent data

60. Artifacts: Abrupt High Contrast Changes Examples: prostheses dental fillings surgical clips Electrodes bone Metal absorbs all radiation in ray causes star-shaped artifact Artifact can be reduced by software

61. CT Artifacts:Beam Hardening Increase in mean energy of polychromatic beam as it passes through patient Can cause broad dark bands or streaks cupping artifact Minimized using beam hardening correction algorithms

62. CT Artifacts:Partial Volume Effect CT #’s based on linear attenuation coefficient for tissue voxels If voxel non-uniform (contains several materials), detection process will average

63. Partial Volume Effect Can appear as incorrect densities streaks bands Minimizing Use thinner slices

64. Image Artifacts:Ring Artifact in 3rd Generation Causes 1 or more bad detectors small offset or gain difference of 1 detector compared to neighbors detector calibration required Reason: Rays measured by a given detector are all tangent to same circle

65. Quality Control in CT Performance tested at regular prescribed intervals